For environmental reasons, it has become increasingly desirable for automotive engines to use blends of fuel which are less polluting. For example, some blends of fuels now include alcohol, and many automotive engines are able to run acceptably well on fuel that contains a certain percentage of alcohol.
Engine performance can be optimized when the percentage of alcohol in the fuel is known, but this is usually not the case. Hence, engine strategies have usually been selected to accommodate fuels with no alcohol or only a limited percentage of alcohol. To adapt an engine to a greater range of alcohol percentages, it has been necessary to know the fuel's actual percentage of alcohol. This means knowing not only the percentage of alcohol being pumped into the vehicle's fuel tank, but the actual percentage of alcohol in the fuel being fed to the engine. For example, if a fuel tank is half full of fuel containing no alcohol, and an operator fills the tank with fuel containing 50% alcohol, the engine will soon be receiving fuel whose alcohol content is roughly 25%. To measure this fuel composition, various types of chemical fuel sensors have been proposed, but they tend to be undesirably expensive.
Even having a good chemical fuel sensor does not resolve all the problems. To calculate the proper engine strategy (e.g. air-to-fuel ration, ignition timing, etc.) to accommodate a particular blend of fuel requires an on-board processor that uses a complicated mathematical model and lengthy calculations. Moreover, these calculations typically require accurate inputs from engine sensors, thus necessitating the use of fairly expensive sensors. Even with such sensors, the lengthy calculations tend to slow the processor's throughput, all of which leads to the conclusion that conventional techniques for dealing with different blends of fuels are inadequate.